class_docstr: Decoder for the Masked Autoencoder model [0].
  
  Decodes encoded patches and predicts pixel values for every patch.
  Code inspired by [1].
  
  - [0]: Masked Autoencoder, 2021, https://arxiv.org/abs/2111.06377
  - [1]: https://github.com/facebookresearch/mae
  
  Attributes:
      seq_length:
          Token sequence length, including the class token.
      num_layers:
          Number of transformer blocks.
      num_heads:
          Number of attention heads.
      embed_input_dim:
          Dimension of the input tokens. Usually be equal to the hidden
          dimension of the MAEEncoder or MAEBackbone.
      hidden_dim:
          Dimension of the decoder tokens.
      mlp_dim:
          Dimension of the MLP in the transformer block.
      out_dim:
          Output dimension of the prediction for a single patch. Usually equal
          to (3 * patch_size ** 2).
      dropout:
          Percentage of elements set to zero after the MLP in the transformer.
      attention_dropout:
          Percentage of elements set to zero after the attention head.
docstr: Embeds encoded input tokens into decoder token dimension.
  
  This is a single linear layer that changes the token dimension from
  embed_input_dim to hidden_dim.
  
  Args:
      input:
          Tensor with shape (batch_size, seq_length, embed_input_dim)
          containing the encoded tokens.
  
  Returns:
      Tensor with shape (batch_size, seq_length, hidden_dim) containing
      the embedded tokens.
docstr_tok: ["Embeds","encoded","input","tokens","into","decoder","token","dimension",".","This","is","a","single","linear","layer","that","changes","the","token","dimension","from","embed_input_dim","to","hidden_dim",".","Args",":","input",":","Tensor","with","shape","(","batch_size",",","seq_length",",","embed_input_dim",")","containing","the","encoded","tokens",".","Returns",":","Tensor","with","shape","(","batch_size",",","seq_length",",","hidden_dim",")","containing","the","embedded","tokens","."]
code: def embed(self, input: torch.Tensor) -> torch.Tensor:
      """Embeds encoded input tokens into decoder token dimension.
  
      This is a single linear layer that changes the token dimension from
      embed_input_dim to hidden_dim.
  
      Args:
          input:
              Tensor with shape (batch_size, seq_length, embed_input_dim)
              containing the encoded tokens.
  
      Returns:
          Tensor with shape (batch_size, seq_length, hidden_dim) containing
          the embedded tokens.
  
      """
      return self.decoder_embed(input)
  
imports: from __future__ import annotations
  import math
  from functools import partial
  from typing import Callable, List, Optional
  import torch
  import torch.nn
  from torchvision.models import vision_transformer
  from torchvision.models.vision_transformer import ConvStemConfig
  from lightly.models import utils
label_desc: Use image node_id 3 for calling the MAEDecoder obj's underlying member method code with example usage: obj.embed(input) and returns: self